Abstract: In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Abstract: A SCADA (Supervisory Control And Data
Acquisition) system is an industrial control and monitoring system for
national infrastructures. The SCADA systems were used in a closed
environment without considering about security functionality in the
past. As communication technology develops, they try to connect the
SCADA systems to an open network. Therefore, the security of the
SCADA systems has been an issue. The study of key management for
SCADA system also has been performed. However, existing key
management schemes for SCADA system such as SKE(Key
establishment for SCADA systems) and SKMA(Key management
scheme for SCADA systems) cannot support broadcasting
communication. To solve this problem, an Advanced Key
Management Architecture for Secure SCADA Communication has
been proposed by Choi et al.. Choi et al.-s scheme also has a problem
that it requires lots of computational cost for multicasting
communication. In this paper, we propose an enhanced scheme which
improving computational cost for multicasting communication with
considering the number of keys to be stored in a low power
communication device (RTU).
Abstract: The expectation of network performance from the
early days of ARPANET until now has been changed significantly.
Every day, new advancement in technological infrastructure opens
the doors for better quality of service and accordingly level of
perceived quality of network services have been increased over the
time. Nowadays for many applications, late information has no value
or even may result in financial or catastrophic loss, on the other hand,
demands for some level of guarantee in providing and maintaining
quality of service are ever increasing. Based on this history, having a
QoS aware routing system which is able to provide today's required
level of quality of service in the networks and effectively adapt to the
future needs, seems as a key requirement for future Internet. In this
work we have extended the traditional AntNet routing system to
support QoS with multiple metrics such as bandwidth and delay
which is named Q-Net. This novel scalable QoS routing system aims
to provide different types of services in the network simultaneously.
Each type of service can be provided for a period of time in the
network and network nodes do not need to have any previous
knowledge about it. When a type of quality of service is requested,
Q-Net will allocate required resources for the service and will
guarantee QoS requirement of the service, based on target objectives.
Abstract: In this paper is being described a possible use of
virtualization technology in teaching computer networks. The
virtualization can be used as a suitable tool for creating virtual
network laboratories, supplementing the real laboratories and
network simulation software in teaching networking concepts. It will
be given a short description of characteristic projects in the area of
virtualization technology usage in network simulation, network
experiments and engineering education. A method for implementing
laboratory has also been explained, together with possible laboratory
usage and design of laboratory exercises. At the end, the laboratory
test results of virtual laboratory are presented as well.
Abstract: For future Broad band ISDN, Asynchronous Transfer
Mode (ATM) is designed not only to support a wide range of traffic
classes with diverse flow characteristics, but also to guarantee the
different quality of service QOS requirements. The QOS may be
measured in terms of cell loss probability and maximum cell delay.
In this paper, ATM networks in which the virtual path (VP)
concept is implemented are considered. By applying the Markov
Deterministic process method, an efficient algorithm to compute the
minimum capacity required to satisfy the QOS requirements when
multiple classes of on-off are multiplexed on to a single VP. Using
the result, we then proposed a simple algorithm to determine different
combinations of VP to achieve the optimum of the total capacity
required for satisfying the individual QOS requirements (loss- delay).
Abstract: The growth of open networks created the interest to
commercialise it. The establishment of an electronic business
mechanism must be accompanied by a digital – electronic payment
system to transfer the value of transactions. Financial organizations
are requested to offer a secure e-payment synthesis with equivalent
level of security served in conventional paper-based payment
transactions. PKI, which is functioning as a chain of trust in security
architecture, can enable security services of cryptography to epayments,
in order to take advantage of the wider base either of
customer or of trading partners and the reduction of cost transaction
achieved by the use of Internet channels. The paper addresses the
possibilities and the implementation suggestions of PKI in relevance
to electronic payments by suggesting a framework that should be
followed.
Abstract: One of the major parts of a jet engine is air intake,
which provides proper and required amount of air for the engine to
operate. There are several aerodynamic parameters which should be
considered in design, such as distortion, pressure recovery, etc. In
this research, the effects of lip ice accretion on pitot intake
performance are investigated. For ice accretion phenomenon, two
supervised multilayer neural networks (ANN) are designed, one for
ice shape prediction and another one for ice roughness estimation
based on experimental data. The Fourier coefficients of transformed
ice shape and parameters include velocity, liquid water content
(LWC), median volumetric diameter (MVD), spray time and
temperature are used in neural network training. Then, the subsonic
intake flow field is simulated numerically using 2D Navier-Stokes
equations and Finite Volume approach with Hybrid mesh includes
structured and unstructured meshes. The results are obtained in
different angles of attack and the variations of intake aerodynamic
parameters due to icing phenomenon are discussed. The results show
noticeable effects of ice accretion phenomenon on intake behavior.
Abstract: This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.
Abstract: This paper presents an innovative computer system
that contributes to sustainable development of the enterprise. The
research refers to a rethinking of traditional systems of collaboration
and risk assessment, present in any organization, leading to a
sustainable enterprise. This concept integrates emerging tools that
allow the implementation and exploitation of the collective
intelligence of the enterprise, allowing the exchange of contextual,
agile and simplified information, and collaboration with networks of
customers and partners in an environment where risks are controlled.
Risk assessment is done in a systemic way: the enterprise as the
system compared to the contained departments and the enterprise as a
subsystem compared to: families of international standards and
sustainability-s responsibilities. The enterprise, in this systemic
vision, responds to the requirements that any existing system to
operate continuously in an indefinite future without reaching key
resource depletion. The research is done by integrating collaborative
science, engineering, management, psychology, obtaining thus a
cornerstone of sustainable development of the enterprise.
Abstract: The future of business intelligence (BI) is to integrate
intelligence into operational systems that works in real-time
analyzing small chunks of data based on requirements on continuous
basis. This is moving away from traditional approach of doing
analysis on ad-hoc basis or sporadically in passive and off-line mode
analyzing huge amount data. Various AI techniques such as expert
systems, case-based reasoning, neural-networks play important role
in building business intelligent systems. Since BI involves various
tasks and models various types of problems, hybrid intelligent
techniques can be better choice. Intelligent systems accessible
through web services make it easier to integrate them into existing
operational systems to add intelligence in every business processes.
These can be built to be invoked in modular and distributed way to
work in real time. Functionality of such systems can be extended to
get external inputs compatible with formats like RSS. In this paper,
we describe a framework that use effective combinations of these
techniques, accessible through web services and work in real-time.
We have successfully developed various prototype systems and done
few commercial deployments in the area of personalization and
recommendation on mobile and websites.
Abstract: As the number of networked computers grows,
intrusion detection is an essential component in keeping networks
secure. Various approaches for intrusion detection are currently
being in use with each one has its own merits and demerits. This
paper presents our work to test and improve the performance of a
new class of decision tree c-fuzzy decision tree to detect intrusion.
The work also includes identifying best candidate feature sub set to
build the efficient c-fuzzy decision tree based Intrusion Detection
System (IDS). We investigated the usefulness of c-fuzzy decision
tree for developing IDS with a data partition based on horizontal
fragmentation. Empirical results indicate the usefulness of our
approach in developing the efficient IDS.
Abstract: In this work, we study the problem of determining
the minimum scheduling length that can satisfy end-to-end (ETE)
traffic demand in scheduling-based multihop WSNs with cooperative
multiple-input multiple-output (MIMO) transmission scheme. Specifically,
we present a cross-layer formulation for the joint routing,
scheduling and stream control problem by incorporating various
power and rate adaptation schemes, and taking into account an
antenna beam pattern model and the signal-to-interference-and-noise
(SINR) constraint at the receiver. In the context, we also propose
column generation (CG) solutions to get rid of the complexity
requiring the enumeration of all possible sets of scheduling links.
Abstract: An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).
Abstract: .Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. Implementation method with resource/speed tradeoff is proposed to handle signed decimal numbers. The VHDL coding developed is tested using Xilinx XC V50hq240 Chip. To improve the speed of operation a lookup table method is used. The problems involved in using a lookup table (LUT) for a nonlinear function is discussed. The percentage saving in resource and the improvement in speed with an LUT for a neuron is reported. An attempt is also made to derive a generalized formula for a multi-input neuron that facilitates to estimate approximately the total resource requirement and speed achievable for a given multilayer neural network. This facilitates the designer to choose the FPGA capacity for a given application. Using the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented
Abstract: Wireless mesh networks based on IEEE 802.11
technology are a scalable and efficient solution for next generation
wireless networking to provide wide-area wideband internet access to
a significant number of users. The deployment of these wireless mesh
networks may be within different authorities and without any
planning, they are potentially overlapped partially or completely in
the same service area. The aim of the proposed model is design a new
model to Enhancement Throughput of Unplanned Wireless Mesh
Networks Deployment Using Partitioning Hierarchical Cluster
(PHC), the unplanned deployment of WMNs are determinates there
performance. We use throughput optimization approach to model the
unplanned WMNs deployment problem based on partitioning
hierarchical cluster (PHC) based architecture, in this paper the
researcher used bridge node by allowing interworking traffic between
these WMNs as solution for performance degradation.
Abstract: In this paper, we present a cost-effective wireless
distributed load shedding system for non-emergency scenarios. In
power transformer locations where SCADA system cannot be used,
the proposed solution provides a reasonable alternative that combines
the use of microcontrollers and existing GSM infrastructure to send
early warning SMS messages to users advising them to proactively
reduce their power consumption before system capacity is reached
and systematic power shutdown takes place.
A novel communication protocol and message set have been
devised to handle the messaging between the transformer sites, where
the microcontrollers are located and where the measurements take
place, and the central processing site where the database server is
hosted. Moreover, the system sends warning messages to the endusers
mobile devices that are used as communication terminals. The
system has been implemented and tested via different experimental
results.
Abstract: A novel idea presented in this paper is to combine
multihop routing with single-frequency networks (SFNs) for a
broadcasting scenario. An SFN is a set of multiple nodes that transmit
the same data simultaneously, resulting in transmitter macrodiversity.
Two of the most important performance factors of multihop
networks, node reachability and routing robustness, are analyzed.
Simulation results show that our proposed SFN-D routing algorithm
improves the node reachability by 37 percentage points as compared
to non-SFN multihop routing. It shows a diversity gain of 3.7 dB,
meaning that 3.7 dB lower transmission powers are required for the
same reachability. Even better results are possible for larger
networks. If an important node becomes inactive, this algorithm can
find new routes that a non-SFN scheme would not be able to find.
Thus, two of the major problems in multihopping are addressed;
achieving robust routing as well as improving node reachability or
reducing transmission power.
Abstract: In this paper, we propose a dynamic TDMA slot
reservation (DTSR) protocol for cognitive radio ad hoc networks.
Quality of Service (QoS) guarantee plays a critically important role
in such networks. We consider the problem of providing QoS
guarantee to users as well as to maintain the most efficient use of
scarce bandwidth resources. According to one hop neighboring
information and the bandwidth requirement, our proposed protocol
dynamically changes the frame length and the transmission schedule.
A dynamic frame length expansion and shrinking scheme that
controls the excessive increase of unassigned slots has been
proposed. This method efficiently utilizes the channel bandwidth by
assigning unused slots to new neighboring nodes and increasing the
frame length when the number of slots in the frame is insufficient to
support the neighboring nodes. It also shrinks the frame length when
half of the slots in the frame of a node are empty. An efficient slot
reservation protocol not only guarantees successful data
transmissions without collisions but also enhance channel spatial
reuse to maximize the system throughput. Our proposed scheme,
which provides both QoS guarantee and efficient resource utilization,
be employed to optimize the channel spatial reuse and maximize the
system throughput. Extensive simulation results show that the
proposed mechanism achieves desirable performance in multichannel
multi-rate cognitive radio ad hoc networks.
Abstract: Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.
Abstract: Microstrip lines, widely used for good reason, are
broadband in frequency and provide circuits that are compact and
light in weight. They are generally economical to produce since they
are readily adaptable to hybrid and monolithic integrated circuit (IC)
fabrication technologies at RF and microwave frequencies. Although,
the existing EM simulation models used for the synthesis and
analysis of microstrip lines are reasonably accurate, they are
computationally intensive and time consuming. Neural networks
recently gained attention as fast and flexible vehicles to microwave
modeling, simulation and optimization. After learning and
abstracting from microwave data, through a process called training,
neural network models are used during microwave design to provide
instant answers to the task learned.This paper presents simple and
accurate ANN models for the synthesis and analysis of Microstrip
lines to more accurately compute the characteristic parameters and
the physical dimensions respectively for the required design
specifications.